code: def _extract_all_shapelets(
      x, window_sizes, window_steps, n_timestamps
  ):
      """Extract all the shapelets from a single time series."""
      shapelets = List()  # shapelets
      lengths = List()  # lengths of shapelets
      start_idx = List()  # start index of shapelets (included)
      end_idx = List()  # end index of shapelets (excluded)
  
      for window_size, window_step in zip(window_sizes, window_steps):
          # Derive the new shape and strides
          overlap = window_size - window_step
          shape_new = (
              (n_timestamps - overlap) // window_step,
              window_size // 1,
          )
          strides = x.strides[0]
          strides_new = (window_step * strides, strides)
  
          # Derive strided view of x
          x_strided = as_strided(
              x, shape=shape_new, strides=strides_new
          )
          x_strided = np.copy(x_strided)
  
          # Add shapelets, lengths, start indices and end indices
          shapelets.append(x_strided)
          lengths.append([x_strided.shape[1]] * x_strided.shape[0])
          start_idx.append(
              np.arange(0, n_timestamps - window_size + 1, window_step)
          )
          end_idx.append(
              np.arange(window_size, n_timestamps + 1, window_step)
          )
  
      return shapelets, lengths, start_idx, end_idx
  
imports: from itertools import chain
  from joblib import delayed, Parallel
  from numba import njit, prange
  from numba.typed import List
  import numpy
  from numpy.lib.stride_tricks import as_strided
  from sklearn.base import BaseEstimator
  from sklearn.feature_selection import f_classif, mutual_info_classif
  from sklearn.utils.validation import check_array, check_is_fitted, check_random_state, check_X_y
  from ..base import UnivariateTransformerMixin
  from ..utils.utils import _windowed_view
label_desc: Use image node_id 1 for calling a global function with example usage: _extract_all_shapelets(x, window_sizes, window_steps, n_timestamps) and returns: shapelets,  lengths,  start_idx,  end_idx